{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/T5/Fine_tuning_Dutch_T5_base_on_CNN_Daily_Mail_for_summarization_(on_TPU_using_HuggingFace_Accelerate).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# mindspore DEMO\n",
    "\n",
    "using mindspore and mindnlp to develop a task fine-tuning T5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\u001b[?25hCollecting multidict<7.0,>=4.5 (from aiohttp->datasets->mindnlp)\n",
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      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m126.2/126.2 kB\u001b[0m \u001b[31m20.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hCollecting propcache>=0.2.0 (from aiohttp->datasets->mindnlp)\n",
      "  Downloading http://192.168.0.122:8888/repository/pypi/packages/propcache/0.2.1/propcache-0.2.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (207 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.8/207.8 kB\u001b[0m \u001b[31m39.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hCollecting yarl<2.0,>=1.17.0 (from aiohttp->datasets->mindnlp)\n",
      "  Downloading http://192.168.0.122:8888/repository/pypi/packages/yarl/1.18.3/yarl-1.18.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (317 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m317.3/317.3 kB\u001b[0m \u001b[31m41.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: typing-extensions>=3.7.4.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp) (4.8.0)\n",
      "Collecting sortedcontainers<3.0.0,>=2.1.0 (from hypothesis<7,>=6.14->pyctcdecode->mindnlp)\n",
      "  Downloading http://192.168.0.122:8888/repository/pypi/packages/sortedcontainers/2.4.0/sortedcontainers-2.4.0-py2.py3-none-any.whl (29 kB)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pandas->datasets->mindnlp) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pandas->datasets->mindnlp) (2023.3.post1)\n",
      "Requirement already satisfied: tzdata>=2022.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pandas->datasets->mindnlp) (2023.3)\n",
      "\u001b[33mDEPRECATION: moxing-framework 2.1.16.2ae09d45 has a non-standard version number. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of moxing-framework or contact the author to suggest that they release a version with a conforming version number. Discussion can be found at https://github.com/pypa/pip/issues/12063\u001b[0m\u001b[33m\n",
      "\u001b[0mInstalling collected packages: sortedcontainers, pygtrie, addict, xxhash, tqdm, tomli, safetensors, requests, pyarrow, propcache, pluggy, multidict, ml-dtypes, iniconfig, hypothesis, frozenlist, dill, async-timeout, aiohappyeyeballs, yarl, pytest, pyctcdecode, multiprocess, mindspore, huggingface-hub, aiosignal, tokenizers, aiohttp, datasets, evaluate, mindnlp\n",
      "  Attempting uninstall: tqdm\n",
      "    Found existing installation: tqdm 4.66.1\n",
      "    Uninstalling tqdm-4.66.1:\n",
      "      Successfully uninstalled tqdm-4.66.1\n",
      "  Attempting uninstall: requests\n",
      "    Found existing installation: requests 2.31.0\n",
      "    Uninstalling requests-2.31.0:\n",
      "      Successfully uninstalled requests-2.31.0\n",
      "  Attempting uninstall: pyarrow\n",
      "    Found existing installation: pyarrow 12.0.1\n",
      "    Uninstalling pyarrow-12.0.1:\n",
      "      Successfully uninstalled pyarrow-12.0.1\n",
      "  Attempting uninstall: mindspore\n",
      "    Found existing installation: mindspore 2.2.0\n",
      "    Uninstalling mindspore-2.2.0:\n",
      "      Successfully uninstalled mindspore-2.2.0\n",
      "  Attempting uninstall: huggingface-hub\n",
      "    Found existing installation: huggingface-hub 0.18.0\n",
      "    Uninstalling huggingface-hub-0.18.0:\n",
      "      Successfully uninstalled huggingface-hub-0.18.0\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "modelarts 1.4.4 requires lxml<=4.7.1, but you have lxml 4.9.3 which is incompatible.\n",
      "modelarts 1.4.4 requires pandas<=1.3.4, but you have pandas 2.1.2 which is incompatible.\n",
      "modelarts 1.4.4 requires Pillow<=9.0.1, but you have pillow 10.0.1 which is incompatible.\n",
      "modelarts 1.4.4 requires pyyaml<=6.0, but you have pyyaml 6.0.1 which is incompatible.\n",
      "modelarts 1.4.4 requires requests<=2.26.0, but you have requests 2.32.3 which is incompatible.\n",
      "modelarts 1.4.4 requires semantic-version<=2.8.5, but you have semantic-version 2.10.0 which is incompatible.\n",
      "modelarts 1.4.4 requires tqdm<=4.62.3, but you have tqdm 4.67.1 which is incompatible.\n",
      "modelarts 1.4.4 requires typing-extensions<=4.0.1, but you have typing-extensions 4.8.0 which is incompatible.\n",
      "modelarts 1.4.4 requires urllib3<=1.26.7, but you have urllib3 2.0.7 which is incompatible.\n",
      "ma-cau 1.1.2 requires click==8.0.3, but you have click 8.1.7 which is incompatible.\n",
      "ma-cau 1.1.2 requires numpy<=1.22.1, but you have numpy 1.26.1 which is incompatible.\n",
      "ma-cau 1.1.2 requires requests==2.26.0, but you have requests 2.32.3 which is incompatible.\n",
      "ma-cau 1.1.2 requires tenacity<=6.1.0, but you have tenacity 8.0.1 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed addict-2.4.0 aiohappyeyeballs-2.4.4 aiohttp-3.11.11 aiosignal-1.3.2 async-timeout-5.0.1 datasets-3.2.0 dill-0.3.8 evaluate-0.4.3 frozenlist-1.5.0 huggingface-hub-0.27.1 hypothesis-6.124.2 iniconfig-2.0.0 mindnlp-0.4.0 mindspore-2.4.10 ml-dtypes-0.5.1 multidict-6.1.0 multiprocess-0.70.16 pluggy-1.5.0 propcache-0.2.1 pyarrow-19.0.0 pyctcdecode-0.5.0 pygtrie-2.5.0 pytest-7.2.0 requests-2.32.3 safetensors-0.5.2 sortedcontainers-2.4.0 tokenizers-0.19.1 tomli-2.2.1 tqdm-4.67.1 xxhash-3.5.0 yarl-1.18.3\n"
     ]
    }
   ],
   "source": [
    "!pip install mindnlp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Cf3PMqPhlWz1"
   },
   "source": [
    "## Load dataset\n",
    "\n",
    "Here we load the Dutch translation of the CNN/Daily Mail dataset, created by the Belgian AI company [ML6](https://www.ml6.eu/). It is hosted on HuggingFace's hub, as can be seen [here](https://huggingface.co/datasets/ml6team/cnn_dailymail_nl)."
   ]
  },
  {
   "cell_type": "code",
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     "base_uri": "https://localhost:8080/",
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    },
    "id": "dOco3waYgyna",
    "outputId": "1c244314-b9d6-4bac-d723-dc330f131e13"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
      "  setattr(self, word, getattr(machar, word).flat[0])\n",
      "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
      "  return self._float_to_str(self.smallest_subnormal)\n",
      "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
      "  setattr(self, word, getattr(machar, word).flat[0])\n",
      "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
      "  return self._float_to_str(self.smallest_subnormal)\n",
      "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "Building prefix dict from the default dictionary ...\n",
      "Dumping model to file cache /tmp/jieba.cache\n",
      "Loading model cost 1.414 seconds.\n",
      "Prefix dict has been built successfully.\n"
     ]
    }
   ],
   "source": [
    "from mindnlp.dataset import load_dataset\n",
    "\n",
    "#train_ds= load_dataset(\"\", split=['train', 'test'])\n",
    "ds = load_dataset(\"abisee/cnn_dailymail\", \"3.0.0\", split=['train', 'validation','test'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SLPVC9zPg67W",
    "outputId": "9b487de2-4ab1-45d7-a8fd-3534b6778354"
   },
   "outputs": [],
   "source": [
    "train = ds['train']\n",
    "val =ds['validation']\n",
    "test = ds['test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mindspore\n",
    "import numpy as np\n",
    "from mindspore.dataset import GeneratorDataset, transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AxvsUvauldNS"
   },
   "source": [
    "Let's look at an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0FA89kduhL_D",
    "outputId": "931a549d-d970-4fde-dbf2-a7cac4161cf6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('LONDON, England (Reuters) -- Harry Potter star Daniel Radcliffe gains access to a reported £20 million ($41.1 million) fortune as he turns 18 on Monday, but he insists the money won\\'t cast a spell on him. Daniel Radcliffe as Harry Potter in \"Harry Potter and the Order of the Phoenix\" To the disappointment of gossip columnists around the world, the young actor says he has no plans to fritter his cash away on fast cars, drink and celebrity parties. \"I don\\'t plan to be one of those people who, as soon as they turn 18, suddenly buy themselves a massive sports car collection or something similar,\" he told an Australian interviewer earlier this month. \"I don\\'t think I\\'ll be particularly extravagant. \"The things I like buying are things that cost about 10 pounds -- books and CDs and DVDs.\" At 18, Radcliffe will be able to gamble in a casino, buy a drink in a pub or see the horror film \"Hostel: Part II,\" currently six places below his number one movie on the UK box office chart. Details of how he\\'ll mark his landmark birthday are under wraps. His agent and publicist had no comment on his plans. \"I\\'ll definitely have some sort of party,\" he said in an interview. \"Hopefully none of you will be reading about it.\" Radcliffe\\'s earnings from the first five Potter films have been held in a trust fund which he has not been able to touch. Despite his growing fame and riches, the actor says he is keeping his feet firmly on the ground. \"People are always looking to say \\'kid star goes off the rails,\\'\" he told reporters last month. \"But I try very hard not to go that way because it would be too easy for them.\" His latest outing as the boy wizard in \"Harry Potter and the Order of the Phoenix\" is breaking records on both sides of the Atlantic and he will reprise the role in the last two films.  Watch I-Reporter give her review of Potter\\'s latest » . There is life beyond Potter, however. The Londoner has filmed a TV movie called \"My Boy Jack,\" about author Rudyard Kipling and his son, due for release later this year. He will also appear in \"December Boys,\" an Australian film about four boys who escape an orphanage. Earlier this year, he made his stage debut playing a tortured teenager in Peter Shaffer\\'s \"Equus.\" Meanwhile, he is braced for even closer media scrutiny now that he\\'s legally an adult: \"I just think I\\'m going to be more sort of fair game,\" he told Reuters. E-mail to a friend . Copyright 2007 Reuters. All rights reserved.This material may not be published, broadcast, rewritten, or redistributed.',\n",
       " \"Harry Potter star Daniel Radcliffe gets £20M fortune as he turns 18 Monday .\\nYoung actor says he has no plans to fritter his cash away .\\nRadcliffe's earnings from first five Potter films have been held in trust fund .\",\n",
       " '42c027e4ff9730fbb3de84c1af0d2c506e41c3e4')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example = train[0]\n",
    "example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0FA89kduhL_D",
    "outputId": "931a549d-d970-4fde-dbf2-a7cac4161cf6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Article: LONDON, England (Reuters) -- Harry Potter star Daniel Radcliffe gains access to a reported £20 million ($41.1 million) fortune as he turns 18 on Monday, but he insists the money won't cast a spell on him. Daniel Radcliffe as Harry Potter in \"Harry Potter and the Order of the Phoenix\" To the disappointment of gossip columnists around the world, the young actor says he has no plans to fritter his cash away on fast cars, drink and celebrity parties. \"I don't plan to be one of those people who, as soon as they turn 18, suddenly buy themselves a massive sports car collection or something similar,\" he told an Australian interviewer earlier this month. \"I don't think I'll be particularly extravagant. \"The things I like buying are things that cost about 10 pounds -- books and CDs and DVDs.\" At 18, Radcliffe will be able to gamble in a casino, buy a drink in a pub or see the horror film \"Hostel: Part II,\" currently six places below his number one movie on the UK box office chart. Details of how he'll mark his landmark birthday are under wraps. His agent and publicist had no comment on his plans. \"I'll definitely have some sort of party,\" he said in an interview. \"Hopefully none of you will be reading about it.\" Radcliffe's earnings from the first five Potter films have been held in a trust fund which he has not been able to touch. Despite his growing fame and riches, the actor says he is keeping his feet firmly on the ground. \"People are always looking to say 'kid star goes off the rails,'\" he told reporters last month. \"But I try very hard not to go that way because it would be too easy for them.\" His latest outing as the boy wizard in \"Harry Potter and the Order of the Phoenix\" is breaking records on both sides of the Atlantic and he will reprise the role in the last two films.  Watch I-Reporter give her review of Potter's latest » . There is life beyond Potter, however. The Londoner has filmed a TV movie called \"My Boy Jack,\" about author Rudyard Kipling and his son, due for release later this year. He will also appear in \"December Boys,\" an Australian film about four boys who escape an orphanage. Earlier this year, he made his stage debut playing a tortured teenager in Peter Shaffer's \"Equus.\" Meanwhile, he is braced for even closer media scrutiny now that he's legally an adult: \"I just think I'm going to be more sort of fair game,\" he told Reuters. E-mail to a friend . Copyright 2007 Reuters. All rights reserved.This material may not be published, broadcast, rewritten, or redistributed.\n",
      "Summary: Harry Potter star Daniel Radcliffe gets £20M fortune as he turns 18 Monday .\n",
      "Young actor says he has no plans to fritter his cash away .\n",
      "Radcliffe's earnings from first five Potter films have been held in trust fund .\n"
     ]
    }
   ],
   "source": [
    "article = example[0]\n",
    "summary = example[1]\n",
    "print(\"Article:\", article)\n",
    "print(\"Summary:\", summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/mindnlp/transformers/tokenization_utils_base.py:1526: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted, and will be then set to `False` by default. \n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from mindnlp.transformers import AutoTokenizer\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"flax-community/t5-base-dutch\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "prefix = \"Summary: \"\n",
    "max_input_length = 512\n",
    "from mindspore import Tensor\n",
    "max_target_length = 64\n",
    "class MyMapDataset():\n",
    "    def __init__(self,data):\n",
    "        super(MyMapDataset).__init__()\n",
    "        self.data = data\n",
    "    def __getitem__(self, index):\n",
    "        \n",
    "        articles,summaries,_ = self.data[index]\n",
    "        #print(articles)\n",
    "        inputs = [prefix + articles]\n",
    "        model_inputs = tokenizer(inputs, max_length=max_input_length, padding=\"max_length\", truncation=True)\n",
    "        labels = [tokenizer(summaries, max_length=max_target_length, padding=\"max_length\", truncation=True).input_ids]\n",
    "        labels_with_ignore_index = []\n",
    "       \n",
    "        for labels_example in labels:\n",
    "            labels_example = [label if label != 0 else -100 for label in labels_example]\n",
    "            labels_with_ignore_index.append(labels_example)\n",
    "        \n",
    "        \n",
    "        return model_inputs['input_ids'][0],model_inputs['attention_mask'][0],labels_with_ignore_index\n",
    "    def __len__(self):\n",
    "        return len(self.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = MyMapDataset(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_data = MyMapDataset(val)\n",
    "test_data = MyMapDataset(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data =  mindspore.dataset.GeneratorDataset(train_data, column_names=[\"input_ids\",\"attention_mask\",\"labels\"], shuffle=False)\n",
    "#dataset = dataset.map(operations=transforms.TypeCast(mindspore.int32), input_columns=\"label\", output_columns=\"labels\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dreE7nRbkapT"
   },
   "source": [
    "Each example consists of an article and a corresponding summary. Easy, huh? Note that you can train T5 on any text-to-text problem. So it could be text as input as an SQL query (as text) as output, it could be text as input and a question related to that text as output (a task called question generation), etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-SlI-qdCliv2"
   },
   "source": [
    "## Encode the dataset\n",
    "\n",
    "The T5 model, like any other Transformer model, does not directly expect text as input. Rather, it expects `input_ids` and `attention_mask`. The `input_ids` are integer vocabulary indices of the tokens of the text (you can read more about them [here](https://huggingface.co/transformers/glossary.html#input-ids)). As labels, it expects the `input_ids` of the summary. \n",
    "\n",
    "Let's encode them using the tokenizer. We also prepend the input with a so-called task prefix, which the authors of T5 used when fine-tuning the model. Here, the prefix is simply \"Vat samen: \" (which is Dutch for \"Summarize: \"), followed by a long document. Note that this prefix will probably not help a lot, since it has not seen this during pre-training. However, it is definitely useful if you want to fine-tune the English T5 model for summarization, as summarization is also part of its pre-training.\n",
    "\n",
    "As we'll train the model on TPU, we pad both the inputs and targets up to the max length. If we were to train this model on GPUs, we would instead pad them up to the longest in a batch, which is more efficient in terms of memory. However, TPUs don't like that."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "akbnGsHkrDHg"
   },
   "source": [
    "Let's verify an example, by decoding the `input_ids` back to text:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "icmIEznVAlUy"
   },
   "source": [
    "We also decode the `labels` (which aren't set to -100) back to text:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ORv91RxQsiQc"
   },
   "source": [
    "Next, let's set the format to PyTorch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3VWtJG6kzpDO",
    "outputId": "61478f8f-f353-435a-df27-ee8674c5f2b4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples: 287113\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of training examples:\", len(train_data))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dA1SvGNazF64"
   },
   "source": [
    "We define a function to create PyTorch dataloaders."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yuj0Cj0WpIQJ"
   },
   "source": [
    "## Fine-tune a model \n",
    "\n",
    "Below, we define a `training_function`, which defines a regular training loop in native PyTorch. We only need to add a few lines to make sure the code will run on TPU. The Accelerator object will take care of that. Basically, the model as well as the data will be replicated across each of the 8 TPU cores. \n",
    "\n",
    "We also define a dictionary of training-related hyperparameters, which we can easily tweak."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "l3Mn8ziqr0wu"
   },
   "outputs": [],
   "source": [
    "hyperparameters = {\n",
    "    \"learning_rate\": 0.0001,\n",
    "    \"num_epochs\": 1, # set to very high number\n",
    "    \"seed\": 42,\n",
    "    \"patience\": 3, # early stopping\n",
    "    \"output_dir\": \"/content/\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "T5ForConditionalGeneration has generative capabilities, as `prepare_inputs_for_generation` is explicitly overwritten. However, it doesn't directly inherit from `GenerationMixin`.`PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.\n",
      "  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).\n",
      "  - If you are not the owner of the model architecture class, please contact the model code owner to update it.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MS_ALLOC_CONF]Runtime config:  enable_vmm:True  vmm_align_size:2MB\n"
     ]
    }
   ],
   "source": [
    "from mindnlp.transformers import T5ForConditionalGeneration\n",
    "model = T5ForConditionalGeneration.from_pretrained(\"flax-community/t5-base-dutch\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = model(input_ids= Tensor(train_data[0][0]),attention_mask = Tensor(train_data[0][1]),\n",
    "         decoder_input_ids = Tensor(train_data[0][2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.trainable_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PEvLESbulv-b",
    "outputId": "f8927534-72e7-4cb4-c149-75cb19e3eda9"
   },
   "outputs": [],
   "source": [
    "import mindspore\n",
    "from mindnlp.transformers import T5ForConditionalGeneration\n",
    "\n",
    "from mindnlp.core import nn, ops, optim\n",
    "\n",
    "from tqdm import tqdm\n",
    "import mindnlp.transformers\n",
    "from mindspore import value_and_grad\n",
    "model = T5ForConditionalGeneration.from_pretrained(\"flax-community/t5-base-dutch\")\n",
    "\n",
    "def forward_fn(data, labels):\n",
    "    input_ids = Tensor(data[0])\n",
    "    attention_mask = Tensor(data[1])\n",
    "    labels = Tensor(labels)\n",
    "    outputs = model(input_ids= input_ids,attention_mask =attention_mask,labels = labels)\n",
    "    loss = outputs.loss\n",
    "    #print(loss)\n",
    "    return loss\n",
    "\n",
    "\n",
    "def training_function(train_dataset,test_dataset):\n",
    "   \n",
    "\n",
    "    # Instantiate optimizer\n",
    "    optimizer = optim.AdamW(\n",
    "                        model.trainable_params(),\n",
    "        lr=hyperparameters[\"learning_rate\"])\n",
    "\n",
    "  \n",
    "    \n",
    "    # Now we train the model\n",
    "    epochs_no_improve = 0\n",
    "    min_val_loss = 1000000\n",
    "    weights = ()\n",
    "    for group in optimizer.param_groups:\n",
    "            weights += tuple(group['params'])\n",
    "            \n",
    "    grad_fn = value_and_grad(forward_fn,grad_position=1)\n",
    "    running_loss = 0.\n",
    "    for epoch in range(hyperparameters[\"num_epochs\"]):\n",
    "        \n",
    "        model.train()\n",
    "        running_loss = 0.\n",
    "        for i in tqdm(range(200)):\n",
    "            data,label = (train_data[i][0],train_data[i][1]),train_data[i][2]\n",
    "            \n",
    "            \n",
    "            #loss = forward_fn(data,label)\n",
    "            loss, grads = grad_fn(data, label)\n",
    "            running_loss += loss.asnumpy()\n",
    "            \n",
    "            \n",
    "            optimizer.step(grads)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SFv3QFEVF16U"
   },
   "source": [
    "Next, we can easily start training by wrapping the `training_function` in a `notebook_launcher`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
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      "b948cb48d00044e3b02954e9709acfca",
      "500f89d42b5e498591f8f60450412156",
      "845fc4356a21441ea82c5f6865804b61",
      "4c734704a469449cbf72a978ebd1bda4",
      "e78ea2cbf6c040ae852dccb8d3ced535"
     ]
    },
    "id": "SyFsYRCOrp-g",
    "outputId": "ccdb3052-312b-4c83-f406-3f7139429d94"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 200/200 [18:14<00:00,  5.47s/it]\n"
     ]
    }
   ],
   "source": [
    "training_function(train_data,test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RmExPYic6EBL"
   },
   "source": [
    "## Inference\n",
    "\n",
    "Now that we have a trained model, let's use it to generate a summary on a new, unseen text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "yd0La4YY4ixe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "editing. AI verkent acute science and medical healthcare applications, such as big data, and receives tests to treatments..\n"
     ]
    }
   ],
   "source": [
    "text = \"\"\"Artificial Intelligence (AI) has rapidly evolved and become an integral part of various industries, \n",
    "particularly healthcare. AI's ability to analyze large volumes of data and identify patterns that humans might miss \n",
    "has revolutionized many aspects of medical care, \n",
    "from diagnostics to treatment planning and patient monitoring.\n",
    "One of the most significant contributions of AI in healthcare is in the field of diagnostics. \n",
    "Machine learning algorithms, a subset of AI, have been trained to interpret medical imaging data, such as X-rays, MRIs, and CT scans.\n",
    "These algorithms can often detect abnormalities, such as tumors or fractures, with higher accuracy and speed than human radiologists. \n",
    "This not only improves the speed of diagnosis but also reduces the potential for human error, leading to better patient outcomes.\n",
    "AI has also shown promise in personalized medicine, where treatments are tailored to an individual's genetic makeup and health history.\n",
    "By analyzing patient data, including genetic information, \n",
    "AI systems can recommend specific treatment plans that are more likely to be effective. \n",
    "This can greatly enhance the precision of treatments, reduce side effects, and improve overall patient care..\"\"\"\n",
    "\n",
    "#trained_model = T5ForConditionalGeneration.from_pretrained(hyperparameters[\"output_dir\"])\n",
    "\n",
    "input_ids = tokenizer(text, max_length=max_input_length, padding=\"max_length\", truncation=True).input_ids\n",
    "input_ids = Tensor([input_ids])\n",
    "generated_ids = model.generate(input_ids, do_sample=True, \n",
    "    max_length=50, \n",
    "    top_k=0, \n",
    "    temperature=0.7\n",
    ")\n",
    "\n",
    "summary = tokenizer.decode(generated_ids.squeeze(), skip_special_tokens=True)\n",
    "print(summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3yO5JnrPRSI2"
   },
   "source": [
    "Make sure to upload the trained model to the hub, such that other people can re-use it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "uIkZRa3hHdd4"
   },
   "outputs": [],
   "source": []
  }
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   "name": "Fine-tuning Dutch T5-base on CNN/Daily Mail for summarization (on TPU using HuggingFace Accelerate).ipynb",
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